Journal of the American Statistical Association

最新文章

• Comment for Data Fission: Splitting a Single Data Point AI翻译
  作者：Hyun, Sangwon
  作者单位：University of California System; University of California Santa Cruz
• A Unified Framework for Rerandomization using Quadratic Forms AI翻译
  作者：Schindl, Kyle; Branson, Zach
  作者单位：Iowa State University; Carnegie Mellon University
  摘要：When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified level of covariate balance is satisfied; this strategy is known as rerandomization. Most rerandomization methods use balance metrics based on a quadratic form v(T)Av , where v is a vector of covariate mean differences and A is a positive semi-definite matrix. In this work, we derive general results for treatment-versus-...
• Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage AI翻译
  作者：Dun, Yuzheng; Chatterjee, Nilanjan; Jin, Jin; Nishimura, Akihiko
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine; University of Pennsylvania; Pennsylvania Medicine
  摘要：Polygenic risk scores (PRS) developed from genome-wide association studies (GWAS) can be used for risk stratification by quantifying the genetic contribution to disease, and many clinical applications have been proposed. Bayesian methods are popular for building PRS because of their natural ability to regularize models and incorporate external information. In this article, we present new theoretical results, methods, and extensive numerical studies to advance Bayesian methods for PRS applicati...
• Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis AI翻译
  作者：Xu, Yuliang; Johnson, Timothy D.; Nichols, Thomas E.; Kang, Jian
  作者单位：University of Chicago; University of Michigan System; University of Michigan; University of Oxford
  摘要：Bayesian Image-on-Scalar Regression (ISR) provides flexible, uncertainty-aware neuroimaging analysis. However, applying ISR to large-scale datasets such as the UK Biobank is challenging due to intensive computational demands and the need to handle subject-specific brain masks rather than a common mask. We propose a novel Bayesian ISR model that scales efficiently while accommodating these inconsistent masks. Our method leverages Gaussian process priors with salience area indicators and introdu...
• Translating Predictive Distributions into Informative Priors AI翻译
  作者：Manderson, Andrew A.; Goudie, Robert J. B.
  作者单位：MRC Biostatistics Unit; University of Cambridge
  摘要：When complex Bayesian models exhibit implausible behavior, one solution is to assemble available information into an informative prior. Challenges arise as prior information is often only available for the observable quantity, or some model-derived marginal quantity, rather than directly pertaining to the (usually latent) parameters in our model. We propose a method for translating available prior information, in the form of an elicited distribution for the observable or model-derived marginal...
• Subtype-Aware Registration of Longitudinal Electronic Health Records AI翻译
  作者：Gai, Xin; Jiang, Shiyi; Zhang, Anru R.
  作者单位：Vanderbilt University; Duke University; Duke University; Duke University
  摘要：Electronic Health Records (EHRs) contain extensive patient information that can inform downstream clinical decisions, such as mortality prediction, disease phenotyping, and disease onset prediction. A key challenge in EHR data analysis is the temporal gap between when a condition is first recorded and its actual onset time. Such timeline misalignment can lead to artificially distinct biomarker trends among patients with similar disease progression, undermining the reliability of downstream ana...
• Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition AI翻译
  作者：Montanari, Andrea; Wu, Yuchen
  作者单位：Stanford University; Stanford University; University of Pennsylvania
  摘要：We consider the problem of sampling from the posterior distribution of a d-dimensional coefficient vector theta, given linear observations y=X theta+epsilon. For sparse Bayesian models, such posteriors are in general multimodal, and therefore challenging to sample from. This observation has prompted the exploration of various heuristics that aim at approximating the posterior distribution. In this article, we study a different approach based on decomposing the posterior distribution into a log...
• Quantification of Vaccine Waning as a Challenge Effect (vol 120 , pg 96 , 2025 ) AI翻译
  作者：Janvin, Matias; Stensrud, Mats J.
  作者单位：University of Oslo; Diakonhjemmet Hospital; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
• Financial Data Analytics with R: Monte-Carlo Validation AI翻译
  作者：Sit, Tony
  作者单位：Chinese University of Hong Kong
• Statistical Prediction and Machine Learning AI翻译
  作者：Pesta, Michal
  作者单位：Charles University Prague